Heavy metal (like Pb, Cd, Cu, et al) in soil has affected the human health for a long time. So, the detection and prevention of soil Pb content have been a hot topic at home and abroad. Traditional methods of soil heavy metal detection, such as atomic absorption spectrometry (AAS), X fluorescence spectrometry (XRFS), are high cost, complicated, time-consuming, cannot meet the requirements of rapid analysis, and is easy to form secondary pollution of samples. Laser-induced breakdown spectroscopy (LIBS), a typical atomic emission spectrum, is a combination of laser technology and spectroscopic technology. It is based on the analysis of characteristic spectral line information that is excited to emit atoms and ions in a substance, and then compositions of the substance were studied. LIBS technology can rapidly detect the composition and content of material elements in any state (solid, liquid and gaseous). It is regarded as an emerging technology in the field of future chemical detection and rapid green analysis. LIBS technology has the advantages of simple pre-processing (or no processing required) for samples, multi-element simultaneous analysis, long-distance measurement, and wide applicability. Based on those advantages, it is widely used in various fields and viewed as one of the research hotspots. Under the background of agricultural informatization, the elements of Pb in the soil will be considered as the research carriers. And laser-induced breakdown spectroscopy (LIBS) technique combined with theoretical analysis and mathematical modeling will be employed to accurately detect the contents of Pb content. Then, the univariate calibration curve methods were built to predict heavy metal Pb content. Firstly, 15 soil samples with known Pb concentration gradient were selected for analysis. Soil LIBS spectral data were pretreated with different pre-processing methods. Three models based on LIBS intensity, peak areas, Lorentz fit intensity after normalized corresponding Pb content was established and fitted to analyze Pb content in soil quantitatively. The results show that the R2 of soil Pb content prediction based on three calibration curve models are 0.918 0, 0.910 1 and 0.914 3, respectively. The results of the three calibration curve analysis methods are good. It indicates that LIBS combined with univariate calibration curve method showed high reliability in detecting Pb in soil. The research results provide the theoretical foundation for developing the diagnosis and prevention technology of heavy metal contamination in soil and offer technical support for scientific spraying and precision management in agricultural production.